Overview

Dataset statistics

Number of variables20
Number of observations2988181
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory456.0 MiB
Average record size in memory160.0 B

Variable types

Numeric15
Categorical5

Alerts

publisher_id has constant value "0" Constant
session_start has a high cardinality: 646874 distinct values High cardinality
click_timestamp has a high cardinality: 2983198 distinct values High cardinality
created_at_ts has a high cardinality: 45785 distinct values High cardinality
delta_timestamp has a high cardinality: 845906 distinct values High cardinality
session_end has a high cardinality: 1409401 distinct values High cardinality
Unnamed: 0 is highly correlated with session_idHigh correlation
session_id is highly correlated with Unnamed: 0High correlation
article_id is highly correlated with category_idHigh correlation
category_id is highly correlated with article_idHigh correlation
Unnamed: 0 is highly correlated with session_idHigh correlation
session_id is highly correlated with Unnamed: 0High correlation
article_id is highly correlated with category_idHigh correlation
click_deviceGroup is highly correlated with click_osHigh correlation
click_os is highly correlated with click_deviceGroupHigh correlation
category_id is highly correlated with article_idHigh correlation
Unnamed: 0 is highly correlated with session_idHigh correlation
session_id is highly correlated with Unnamed: 0High correlation
article_id is highly correlated with category_idHigh correlation
category_id is highly correlated with article_idHigh correlation
Unnamed: 0 is highly correlated with session_idHigh correlation
user_id is highly correlated with session_idHigh correlation
session_id is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
article_id is highly correlated with category_idHigh correlation
click_deviceGroup is highly correlated with click_osHigh correlation
click_os is highly correlated with click_deviceGroupHigh correlation
click_country is highly correlated with click_regionHigh correlation
click_region is highly correlated with click_countryHigh correlation
category_id is highly correlated with article_idHigh correlation
Unnamed: 0 is uniformly distributed Uniform
click_timestamp is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
publisher_id has 2988181 (100.0%) zeros Zeros

Reproduction

Analysis started2022-10-02 19:52:30.641737
Analysis finished2022-10-02 20:01:24.879740
Duration8 minutes and 54.24 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2988181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1494090
Minimum0
Maximum2988180
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:26.492740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile149409
Q1747045
median1494090
Q32241135
95-th percentile2838771
Maximum2988180
Range2988180
Interquartile range (IQR)1494090

Descriptive statistics

Standard deviation862613.6967
Coefficient of variation (CV)0.577350559
Kurtosis-1.2
Mean1494090
Median Absolute Deviation (MAD)747045
Skewness-1.014664281 × 10-15
Sum4.46461135 × 1012
Variance7.441023897 × 1011
MonotonicityStrictly increasing
2022-10-02T22:01:26.823738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
19921141
 
< 0.1%
19921161
 
< 0.1%
19921171
 
< 0.1%
19921181
 
< 0.1%
19921191
 
< 0.1%
19921201
 
< 0.1%
19921211
 
< 0.1%
19921221
 
< 0.1%
19921231
 
< 0.1%
Other values (2988171)2988171
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
29881801
< 0.1%
29881791
< 0.1%
29881781
< 0.1%
29881771
< 0.1%
29881761
< 0.1%
29881751
< 0.1%
29881741
< 0.1%
29881731
< 0.1%
29881721
< 0.1%
29881711
< 0.1%

user_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct322897
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107947.8258
Minimum0
Maximum322896
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:27.241776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6370
Q140341
median86229
Q3163261
95-th percentile274162
Maximum322896
Range322896
Interquartile range (IQR)122920

Descriptive statistics

Standard deviation83648.36147
Coefficient of variation (CV)0.7748962136
Kurtosis-0.4686650537
Mean107947.8258
Median Absolute Deviation (MAD)57248
Skewness0.7231189115
Sum3.22567642 × 1011
Variance6997048377
MonotonicityNot monotonic
2022-10-02T22:01:27.568738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58901232
 
< 0.1%
73574939
 
< 0.1%
15867900
 
< 0.1%
80350783
 
< 0.1%
15275746
 
< 0.1%
2151722
 
< 0.1%
4568529
 
< 0.1%
12897513
 
< 0.1%
11521502
 
< 0.1%
34541501
 
< 0.1%
Other values (322887)2980814
99.8%
ValueCountFrequency (%)
08
 
< 0.1%
112
 
< 0.1%
24
 
< 0.1%
317
 
< 0.1%
47
 
< 0.1%
587
< 0.1%
635
< 0.1%
722
 
< 0.1%
856
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
3228962
< 0.1%
3228952
< 0.1%
3228942
< 0.1%
3228932
< 0.1%
3228922
< 0.1%
3228912
< 0.1%
3228902
< 0.1%
3228892
< 0.1%
3228882
< 0.1%
3228873
< 0.1%

session_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1048594
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.507472228 × 1015
Minimum1.506825423 × 1015
Maximum1.508211379 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:28.053740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.506825423 × 1015
5-th percentile1.506941766 × 1015
Q11.507124152 × 1015
median1.50749334 × 1015
Q31.507749414 × 1015
95-th percentile1.508153221 × 1015
Maximum1.508211379 × 1015
Range1.385955918 × 1012
Interquartile range (IQR)6.252618534 × 1011

Descriptive statistics

Standard deviation3.855244602 × 1011
Coefficient of variation (CV)0.0002557423301
Kurtosis-1.111389169
Mean1.507472228 × 1015
Median Absolute Deviation (MAD)3.329949664 × 1011
Skewness0.1807598817
Sum3.594316782 × 1018
Variance1.486291094 × 1023
MonotonicityNot monotonic
2022-10-02T22:01:28.393739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.507563658 × 1015124
 
< 0.1%
1.507896573 × 1015107
 
< 0.1%
1.507133568 × 1015106
 
< 0.1%
1.507309773 × 101598
 
< 0.1%
1.508112331 × 101594
 
< 0.1%
1.507647366 × 101592
 
< 0.1%
1.507475404 × 101586
 
< 0.1%
1.506959499 × 101582
 
< 0.1%
1.508154737 × 101579
 
< 0.1%
1.506999909 × 101575
 
< 0.1%
Other values (1048584)2987238
> 99.9%
ValueCountFrequency (%)
1.506825423 × 10152
< 0.1%
1.506825426 × 10152
< 0.1%
1.506825435 × 10152
< 0.1%
1.506825443 × 10152
< 0.1%
1.506825528 × 10152
< 0.1%
1.506825541 × 10153
< 0.1%
1.506825553 × 10152
< 0.1%
1.506825568 × 10152
< 0.1%
1.506825573 × 10153
< 0.1%
1.506825599 × 10152
< 0.1%
ValueCountFrequency (%)
1.508211379 × 10152
 
< 0.1%
1.508211376 × 10152
 
< 0.1%
1.508211372 × 10152
 
< 0.1%
1.508211369 × 10157
< 0.1%
1.508211367 × 10152
 
< 0.1%
1.508211353 × 10154
< 0.1%
1.508211348 × 10152
 
< 0.1%
1.508211326 × 10152
 
< 0.1%
1.508211326 × 10154
< 0.1%
1.508211324 × 10152
 
< 0.1%

session_start
Categorical

HIGH CARDINALITY

Distinct646874
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
2017-10-09 15:40:57
 
127
2017-10-13 12:09:33
 
112
2017-10-04 16:12:47
 
108
2017-10-06 17:09:33
 
98
2017-10-10 14:56:06
 
97
Other values (646869)
2987639 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters56775439
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-10-01 02:37:03
2nd row2017-10-01 02:42:07
3rd row2017-10-01 02:48:59
4th row2017-10-01 02:49:02
5th row2017-10-01 02:54:23

Common Values

ValueCountFrequency (%)
2017-10-09 15:40:57127
 
< 0.1%
2017-10-13 12:09:33112
 
< 0.1%
2017-10-04 16:12:47108
 
< 0.1%
2017-10-06 17:09:3398
 
< 0.1%
2017-10-10 14:56:0697
 
< 0.1%
2017-10-16 00:05:3196
 
< 0.1%
2017-10-10 16:05:4387
 
< 0.1%
2017-10-02 15:51:3987
 
< 0.1%
2017-10-08 15:10:0386
 
< 0.1%
2017-10-16 11:52:1785
 
< 0.1%
Other values (646864)2987198
> 99.9%

Length

2022-10-02T22:01:28.732737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-10-02305709
 
5.1%
2017-10-10281384
 
4.7%
2017-10-03259709
 
4.3%
2017-10-09249856
 
4.2%
2017-10-11238521
 
4.0%
2017-10-04215267
 
3.6%
2017-10-06207537
 
3.5%
2017-10-16190891
 
3.2%
2017-10-05190074
 
3.2%
2017-10-13180599
 
3.0%
Other values (83818)3656815
61.2%

Most occurring characters

ValueCountFrequency (%)
111434946
20.1%
010649945
18.8%
25982278
10.5%
-5976362
10.5%
:5976362
10.5%
73949992
 
7.0%
2988181
 
5.3%
32391815
 
4.2%
42104829
 
3.7%
52074593
 
3.7%
Other values (3)3246136
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41834534
73.7%
Dash Punctuation5976362
 
10.5%
Other Punctuation5976362
 
10.5%
Space Separator2988181
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
111434946
27.3%
010649945
25.5%
25982278
14.3%
73949992
 
9.4%
32391815
 
5.7%
42104829
 
5.0%
52074593
 
5.0%
61210846
 
2.9%
91111614
 
2.7%
8923676
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-5976362
100.0%
Other Punctuation
ValueCountFrequency (%)
:5976362
100.0%
Space Separator
ValueCountFrequency (%)
2988181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common56775439
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
111434946
20.1%
010649945
18.8%
25982278
10.5%
-5976362
10.5%
:5976362
10.5%
73949992
 
7.0%
2988181
 
5.3%
32391815
 
4.2%
42104829
 
3.7%
52074593
 
3.7%
Other values (3)3246136
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII56775439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111434946
20.1%
010649945
18.8%
25982278
10.5%
-5976362
10.5%
:5976362
10.5%
73949992
 
7.0%
2988181
 
5.3%
32391815
 
4.2%
42104829
 
3.7%
52074593
 
3.7%
Other values (3)3246136
 
5.7%

session_size
Real number (ℝ≥0)

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.901885127
Minimum2
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:29.036739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q34
95-th percentile9
Maximum124
Range122
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.929941495
Coefficient of variation (CV)1.007190465
Kurtosis158.4608899
Mean3.901885127
Median Absolute Deviation (MAD)1
Skewness9.090074854
Sum11659539
Variance15.44444016
MonotonicityNot monotonic
2022-10-02T22:01:29.674738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21260372
42.2%
3670185
22.4%
4374240
 
12.5%
5220105
 
7.4%
6135762
 
4.5%
788354
 
3.0%
858544
 
2.0%
940878
 
1.4%
1029530
 
1.0%
1121714
 
0.7%
Other values (62)88497
 
3.0%
ValueCountFrequency (%)
21260372
42.2%
3670185
22.4%
4374240
 
12.5%
5220105
 
7.4%
6135762
 
4.5%
788354
 
3.0%
858544
 
2.0%
940878
 
1.4%
1029530
 
1.0%
1121714
 
0.7%
ValueCountFrequency (%)
124124
< 0.1%
107107
< 0.1%
106106
< 0.1%
9898
< 0.1%
9494
< 0.1%
9292
< 0.1%
8686
< 0.1%
8282
< 0.1%
7979
< 0.1%
7575
< 0.1%

article_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46033
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194922.6487
Minimum3
Maximum364046
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:29.984738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile42223
Q1124228
median202381
Q3277067
95-th percentile336254
Maximum364046
Range364043
Interquartile range (IQR)152839

Descriptive statistics

Standard deviation90768.42147
Coefficient of variation (CV)0.4656638009
Kurtosis-0.943045904
Mean194922.6487
Median Absolute Deviation (MAD)77632
Skewness-0.1234365434
Sum5.824641553 × 1011
Variance8238906336
MonotonicityNot monotonic
2022-10-02T22:01:30.305738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16097437213
 
1.2%
27214328943
 
1.0%
33622123851
 
0.8%
23469823499
 
0.8%
12390923122
 
0.8%
33622321855
 
0.7%
9621021577
 
0.7%
16265521062
 
0.7%
18317620303
 
0.7%
16862319526
 
0.7%
Other values (46023)2747230
91.9%
ValueCountFrequency (%)
31
< 0.1%
271
< 0.1%
691
< 0.1%
812
< 0.1%
841
< 0.1%
942
< 0.1%
1152
< 0.1%
1251
< 0.1%
1371
< 0.1%
1391
< 0.1%
ValueCountFrequency (%)
3640462
 
< 0.1%
3640438
 
< 0.1%
3640281
 
< 0.1%
3640221
 
< 0.1%
36401722
< 0.1%
3640151
 
< 0.1%
3640141
 
< 0.1%
3640131
 
< 0.1%
3640121
 
< 0.1%
3640014
 
< 0.1%

click_timestamp
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2983198
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
2017-10-03 17:40:48.643
 
3
2017-10-02 16:16:49.961
 
3
2017-10-02 14:54:37.261
 
3
2017-10-13 14:39:48.690
 
3
2017-10-06 20:07:23.928
 
3
Other values (2983193)
2988166 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters68728163
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2978224 ?
Unique (%)99.7%

Sample

1st row2017-10-01 03:00:28.020
2nd row2017-10-01 05:42:28.634
3rd row2017-10-01 11:27:58.141
4th row2017-10-01 03:08:29.970
5th row2017-10-01 03:33:43.469

Common Values

ValueCountFrequency (%)
2017-10-03 17:40:48.6433
 
< 0.1%
2017-10-02 16:16:49.9613
 
< 0.1%
2017-10-02 14:54:37.2613
 
< 0.1%
2017-10-13 14:39:48.6903
 
< 0.1%
2017-10-06 20:07:23.9283
 
< 0.1%
2017-10-09 13:01:34.0453
 
< 0.1%
2017-10-02 20:16:02.2563
 
< 0.1%
2017-10-16 14:42:54.8993
 
< 0.1%
2017-10-14 12:28:25.6563
 
< 0.1%
2017-10-10 01:08:21.5402
 
< 0.1%
Other values (2983188)2988152
> 99.9%

Length

2022-10-02T22:01:30.770738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-10-02303177
 
5.1%
2017-10-10282391
 
4.7%
2017-10-03261159
 
4.4%
2017-10-09248208
 
4.2%
2017-10-11238969
 
4.0%
2017-10-04215415
 
3.6%
2017-10-06207646
 
3.5%
2017-10-05190003
 
3.2%
2017-10-16189779
 
3.2%
2017-10-13180723
 
3.0%
Other values (2923727)3658892
61.2%

Most occurring characters

ValueCountFrequency (%)
112265879
17.8%
011533489
16.8%
26914494
10.1%
-5976362
8.7%
:5976362
8.7%
74849332
 
7.1%
33299611
 
4.8%
43017099
 
4.4%
2988181
 
4.3%
.2988181
 
4.3%
Other values (4)8919173
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50799077
73.9%
Other Punctuation8964543
 
13.0%
Dash Punctuation5976362
 
8.7%
Space Separator2988181
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
112265879
24.1%
011533489
22.7%
26914494
13.6%
74849332
 
9.5%
33299611
 
6.5%
43017099
 
5.9%
52978574
 
5.9%
62106971
 
4.1%
92008543
 
4.0%
81825085
 
3.6%
Other Punctuation
ValueCountFrequency (%)
:5976362
66.7%
.2988181
33.3%
Dash Punctuation
ValueCountFrequency (%)
-5976362
100.0%
Space Separator
ValueCountFrequency (%)
2988181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common68728163
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
112265879
17.8%
011533489
16.8%
26914494
10.1%
-5976362
8.7%
:5976362
8.7%
74849332
 
7.1%
33299611
 
4.8%
43017099
 
4.4%
2988181
 
4.3%
.2988181
 
4.3%
Other values (4)8919173
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII68728163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112265879
17.8%
011533489
16.8%
26914494
10.1%
-5976362
8.7%
:5976362
8.7%
74849332
 
7.1%
33299611
 
4.8%
43017099
 
4.4%
2988181
 
4.3%
.2988181
 
4.3%
Other values (4)8919173
13.0%

click_environment
Real number (ℝ≥0)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.942652068
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:31.048737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.339680408
Coefficient of variation (CV)0.0861553092
Kurtosis33.01323632
Mean3.942652068
Median Absolute Deviation (MAD)0
Skewness-5.848728196
Sum11781358
Variance0.1153827796
MonotonicityNot monotonic
2022-10-02T22:01:31.317741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
42904478
97.2%
279743
 
2.7%
13960
 
0.1%
ValueCountFrequency (%)
13960
 
0.1%
279743
 
2.7%
42904478
97.2%
ValueCountFrequency (%)
42904478
97.2%
279743
 
2.7%
13960
 
0.1%

click_deviceGroup
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.819305792
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:31.588737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile3
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.042213782
Coefficient of variation (CV)0.5728634442
Kurtosis-1.427040365
Mean1.819305792
Median Absolute Deviation (MAD)0
Skewness0.5763858618
Sum5436415
Variance1.086209567
MonotonicityNot monotonic
2022-10-02T22:01:31.860739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
11823162
61.0%
31047086
35.0%
4117640
 
3.9%
5283
 
< 0.1%
210
 
< 0.1%
ValueCountFrequency (%)
11823162
61.0%
210
 
< 0.1%
31047086
35.0%
4117640
 
3.9%
5283
 
< 0.1%
ValueCountFrequency (%)
5283
 
< 0.1%
4117640
 
3.9%
31047086
35.0%
210
 
< 0.1%
11823162
61.0%

click_os
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.27760333
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:32.132738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median17
Q317
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)15

Descriptive statistics

Standard deviation6.881718417
Coefficient of variation (CV)0.5182952258
Kurtosis-0.9317514661
Mean13.27760333
Median Absolute Deviation (MAD)0
Skewness-0.9541171292
Sum39675882
Variance47.35804837
MonotonicityNot monotonic
2022-10-02T22:01:32.403740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
171738138
58.2%
2788699
26.4%
20369586
 
12.4%
1260096
 
2.0%
1323711
 
0.8%
196384
 
0.2%
51513
 
0.1%
354
 
< 0.1%
ValueCountFrequency (%)
2788699
26.4%
354
 
< 0.1%
51513
 
0.1%
1260096
 
2.0%
1323711
 
0.8%
171738138
58.2%
196384
 
0.2%
20369586
 
12.4%
ValueCountFrequency (%)
20369586
 
12.4%
196384
 
0.2%
171738138
58.2%
1323711
 
0.8%
1260096
 
2.0%
51513
 
0.1%
354
 
< 0.1%
2788699
26.4%

click_country
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.357656046
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:32.677740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.725860976
Coefficient of variation (CV)1.271206342
Kurtosis21.55275991
Mean1.357656046
Median Absolute Deviation (MAD)0
Skewness4.802252338
Sum4056922
Variance2.978596109
MonotonicityNot monotonic
2022-10-02T22:01:32.942738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
12852406
95.5%
1061377
 
2.1%
1129999
 
1.0%
89556
 
0.3%
67256
 
0.2%
96746
 
0.2%
26101
 
0.2%
34540
 
0.2%
53498
 
0.1%
43389
 
0.1%
ValueCountFrequency (%)
12852406
95.5%
26101
 
0.2%
34540
 
0.2%
43389
 
0.1%
53498
 
0.1%
67256
 
0.2%
73313
 
0.1%
89556
 
0.3%
96746
 
0.2%
1061377
 
2.1%
ValueCountFrequency (%)
1129999
1.0%
1061377
2.1%
96746
 
0.2%
89556
 
0.3%
73313
 
0.1%
67256
 
0.2%
53498
 
0.1%
43389
 
0.1%
34540
 
0.2%
26101
 
0.2%

click_region
Real number (ℝ≥0)

HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.31331435
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:33.231739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q113
median21
Q325
95-th percentile27
Maximum28
Range27
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.064006436
Coefficient of variation (CV)0.3857306383
Kurtosis-0.9755078164
Mean18.31331435
Median Absolute Deviation (MAD)4
Skewness-0.545880017
Sum54723498
Variance49.90018693
MonotonicityNot monotonic
2022-10-02T22:01:33.538737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
25804985
26.9%
21464230
15.5%
13320957
 
10.7%
8179339
 
6.0%
16164884
 
5.5%
28135793
 
4.5%
24130537
 
4.4%
20120884
 
4.0%
596979
 
3.2%
984693
 
2.8%
Other values (18)484900
16.2%
ValueCountFrequency (%)
17110
 
0.2%
216728
 
0.6%
33997
 
0.1%
430265
 
1.0%
596979
3.2%
657254
 
1.9%
764062
 
2.1%
8179339
6.0%
984693
2.8%
1021995
 
0.7%
ValueCountFrequency (%)
28135793
 
4.5%
2718711
 
0.6%
2618893
 
0.6%
25804985
26.9%
24130537
 
4.4%
2343
 
< 0.1%
2213101
 
0.4%
21464230
15.5%
20120884
 
4.0%
1934092
 
1.1%

click_referrer_type
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.838981307
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:33.815741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.15635571
Coefficient of variation (CV)0.628802319
Kurtosis9.117533472
Mean1.838981307
Median Absolute Deviation (MAD)0
Skewness2.83996653
Sum5495209
Variance1.337158529
MonotonicityNot monotonic
2022-10-02T22:01:34.088741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21602601
53.6%
11194321
40.0%
580766
 
2.7%
769798
 
2.3%
620455
 
0.7%
419820
 
0.7%
3420
 
< 0.1%
ValueCountFrequency (%)
11194321
40.0%
21602601
53.6%
3420
 
< 0.1%
419820
 
0.7%
580766
 
2.7%
620455
 
0.7%
769798
 
2.3%
ValueCountFrequency (%)
769798
 
2.3%
620455
 
0.7%
580766
 
2.7%
419820
 
0.7%
3420
 
< 0.1%
21602601
53.6%
11194321
40.0%

category_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct316
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.9382404
Minimum1
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:34.393740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile67
Q1250
median327
Q3409
95-th percentile437
Maximum460
Range459
Interquartile range (IQR)159

Descriptive statistics

Standard deviation113.0805459
Coefficient of variation (CV)0.3696188674
Kurtosis0.1095423476
Mean305.9382404
Median Absolute Deviation (MAD)77
Skewness-0.8877669712
Sum914198837
Variance12787.20986
MonotonicityNot monotonic
2022-10-02T22:01:34.718740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281370843
 
12.4%
375268257
 
9.0%
412178894
 
6.0%
437157085
 
5.3%
250140454
 
4.7%
331115901
 
3.9%
399104464
 
3.5%
20983750
 
2.8%
41867119
 
2.2%
11864216
 
2.1%
Other values (306)1437198
48.1%
ValueCountFrequency (%)
16107
 
0.2%
23742
 
0.1%
31
 
< 0.1%
42856
 
0.1%
69971
0.3%
719898
0.7%
915470
0.5%
112
 
< 0.1%
1548
 
< 0.1%
16135
 
< 0.1%
ValueCountFrequency (%)
46012
 
< 0.1%
4581230
 
< 0.1%
45611
 
< 0.1%
45511042
0.4%
454102
 
< 0.1%
4535
 
< 0.1%
4512
 
< 0.1%
4504830
0.2%
4493
 
< 0.1%
4484436
0.1%

created_at_ts
Categorical

HIGH CARDINALITY

Distinct45785
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
2017-10-02 02:52:27
 
37213
2017-10-02 16:31:10
 
28943
2017-10-10 05:26:01
 
23851
2017-10-10 06:56:37
 
23499
2017-10-05 10:22:35
 
23122
Other values (45780)
2851553 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters56775439
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24666 ?
Unique (%)0.8%

Sample

1st row2017-09-30 19:41:58
2nd row2017-09-30 19:41:58
3rd row2017-09-30 19:41:58
4th row2017-09-30 19:41:58
5th row2017-09-30 19:41:58

Common Values

ValueCountFrequency (%)
2017-10-02 02:52:2737213
 
1.2%
2017-10-02 16:31:1028943
 
1.0%
2017-10-10 05:26:0123851
 
0.8%
2017-10-10 06:56:3723499
 
0.8%
2017-10-05 10:22:3523122
 
0.8%
2017-10-09 13:28:2021855
 
0.7%
2017-10-12 08:59:5121577
 
0.7%
2017-10-02 13:06:5021062
 
0.7%
2017-10-11 14:18:4920303
 
0.7%
2017-10-04 19:07:3019526
 
0.7%
Other values (45775)2747230
91.9%

Length

2022-10-02T22:01:35.028738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-10-02354788
 
5.9%
2017-10-10270884
 
4.5%
2017-10-09261821
 
4.4%
2017-10-05218982
 
3.7%
2017-10-11218099
 
3.6%
2017-10-04211749
 
3.5%
2017-10-06197795
 
3.3%
2017-10-03179220
 
3.0%
2017-10-13148735
 
2.5%
2017-10-16143295
 
2.4%
Other values (32977)3770994
63.1%

Most occurring characters

ValueCountFrequency (%)
011307282
19.9%
111162604
19.7%
-5976362
10.5%
:5976362
10.5%
25650894
10.0%
74046248
 
7.1%
2988181
 
5.3%
52245202
 
4.0%
31978325
 
3.5%
41844803
 
3.2%
Other values (3)3599176
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number41834534
73.7%
Dash Punctuation5976362
 
10.5%
Other Punctuation5976362
 
10.5%
Space Separator2988181
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011307282
27.0%
111162604
26.7%
25650894
13.5%
74046248
 
9.7%
52245202
 
5.4%
31978325
 
4.7%
41844803
 
4.4%
61315854
 
3.1%
91201572
 
2.9%
81081750
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
-5976362
100.0%
Other Punctuation
ValueCountFrequency (%)
:5976362
100.0%
Space Separator
ValueCountFrequency (%)
2988181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common56775439
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011307282
19.9%
111162604
19.7%
-5976362
10.5%
:5976362
10.5%
25650894
10.0%
74046248
 
7.1%
2988181
 
5.3%
52245202
 
4.0%
31978325
 
3.5%
41844803
 
3.2%
Other values (3)3599176
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII56775439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011307282
19.9%
111162604
19.7%
-5976362
10.5%
:5976362
10.5%
25650894
10.0%
74046248
 
7.1%
2988181
 
5.3%
52245202
 
4.0%
31978325
 
3.5%
41844803
 
3.2%
Other values (3)3599176
 
6.3%

publisher_id
Real number (ℝ≥0)

CONSTANT
REJECTED
ZEROS

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros2988181
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:35.311741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2022-10-02T22:01:35.577739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
02988181
100.0%
ValueCountFrequency (%)
02988181
100.0%
ValueCountFrequency (%)
02988181
100.0%

words_count
Real number (ℝ≥0)

Distinct536
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.6283381
Minimum0
Maximum6690
Zeros65
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.8 MiB
2022-10-02T22:01:36.084740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile136
Q1173
median198
Q3232
95-th percentile284
Maximum6690
Range6690
Interquartile range (IQR)59

Descriptive statistics

Standard deviation81.60152023
Coefficient of variation (CV)0.3911334432
Kurtosis79.67854049
Mean208.6283381
Median Absolute Deviation (MAD)28
Skewness6.55037723
Sum623419236
Variance6658.808105
MonotonicityNot monotonic
2022-10-02T22:01:36.410739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18462994
 
2.1%
15860457
 
2.0%
19758150
 
1.9%
21056408
 
1.9%
25949572
 
1.7%
18344730
 
1.5%
19942994
 
1.4%
20542918
 
1.4%
19840228
 
1.3%
22039454
 
1.3%
Other values (526)2490276
83.3%
ValueCountFrequency (%)
065
 
< 0.1%
51
 
< 0.1%
711
 
< 0.1%
8137
 
< 0.1%
10559
< 0.1%
119
 
< 0.1%
1219
 
< 0.1%
133
 
< 0.1%
141279
< 0.1%
155
 
< 0.1%
ValueCountFrequency (%)
66901
 
< 0.1%
38087
< 0.1%
30825
< 0.1%
28991
 
< 0.1%
27434
< 0.1%
17648
< 0.1%
16762
 
< 0.1%
16353
 
< 0.1%
16262
 
< 0.1%
16061
 
< 0.1%

delta_timestamp
Categorical

HIGH CARDINALITY

Distinct845906
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
0 days 00:00:00
1048606 
0 days 00:00:30
457210 
-1 days +23:59:30
406846 
0 days 00:00:00.001000
 
11
0 days 00:00:00.052000
 
11
Other values (845901)
1075497 

Length

Max length25
Median length15
Mean length18.12865084
Min length15

Characters and Unicode

Total characters54171690
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique674100 ?
Unique (%)22.6%

Sample

1st row0 days 00:00:00
2nd row0 days 00:00:00
3rd row0 days 00:00:00
4th row0 days 00:00:00
5th row0 days 00:00:00

Common Values

ValueCountFrequency (%)
0 days 00:00:001048606
35.1%
0 days 00:00:30457210
15.3%
-1 days +23:59:30406846
 
13.6%
0 days 00:00:00.00100011
 
< 0.1%
0 days 00:00:00.05200011
 
< 0.1%
0 days 00:00:00.06100010
 
< 0.1%
0 days 00:00:00.16700010
 
< 0.1%
0 days 00:00:00.02300010
 
< 0.1%
-1 days +23:59:59.96500010
 
< 0.1%
0 days 00:00:00.00200010
 
< 0.1%
Other values (845896)1075447
36.0%

Length

2022-10-02T22:01:36.753741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
days2988181
33.3%
02072584
23.1%
00:00:001048606
 
11.7%
1913777
 
10.2%
00:00:30457210
 
5.1%
23:59:30406846
 
4.5%
21033
 
< 0.1%
3371
 
< 0.1%
4171
 
< 0.1%
5118
 
< 0.1%
Other values (845766)1075646
 
12.0%

Most occurring characters

ValueCountFrequency (%)
016508571
30.5%
5976362
 
11.0%
:5976362
 
11.0%
d2988181
 
5.5%
a2988181
 
5.5%
y2988181
 
5.5%
s2988181
 
5.5%
32512041
 
4.6%
11775907
 
3.3%
21717247
 
3.2%
Other values (9)7752476
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27363959
50.5%
Lowercase Letter11952724
22.1%
Other Punctuation7050807
 
13.0%
Space Separator5976362
 
11.0%
Dash Punctuation913919
 
1.7%
Math Symbol913919
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016508571
60.3%
32512041
 
9.2%
11775907
 
6.5%
21717247
 
6.3%
51481205
 
5.4%
9957894
 
3.5%
4792265
 
2.9%
8550630
 
2.0%
7539231
 
2.0%
6528968
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
d2988181
25.0%
a2988181
25.0%
y2988181
25.0%
s2988181
25.0%
Other Punctuation
ValueCountFrequency (%)
:5976362
84.8%
.1074445
 
15.2%
Space Separator
ValueCountFrequency (%)
5976362
100.0%
Dash Punctuation
ValueCountFrequency (%)
-913919
100.0%
Math Symbol
ValueCountFrequency (%)
+913919
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common42218966
77.9%
Latin11952724
 
22.1%

Most frequent character per script

Common
ValueCountFrequency (%)
016508571
39.1%
5976362
 
14.2%
:5976362
 
14.2%
32512041
 
6.0%
11775907
 
4.2%
21717247
 
4.1%
51481205
 
3.5%
.1074445
 
2.5%
9957894
 
2.3%
-913919
 
2.2%
Other values (5)3325013
 
7.9%
Latin
ValueCountFrequency (%)
d2988181
25.0%
a2988181
25.0%
y2988181
25.0%
s2988181
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII54171690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016508571
30.5%
5976362
 
11.0%
:5976362
 
11.0%
d2988181
 
5.5%
a2988181
 
5.5%
y2988181
 
5.5%
s2988181
 
5.5%
32512041
 
4.6%
11775907
 
3.3%
21717247
 
3.2%
Other values (9)7752476
14.3%

session_end
Categorical

HIGH CARDINALITY

Distinct1409401
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
0 days 00:00:00.826000
 
62
0 days 00:00:00.359000
 
62
0 days 00:00:00.837000
 
62
0 days 00:00:00.973000
 
61
0 days 00:00:00.767000
 
60
Other values (1409396)
2987874 

Length

Max length23
Median length22
Mean length21.99319218
Min length15

Characters and Unicode

Total characters65719639
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique914347 ?
Unique (%)30.6%

Sample

1st row0 days 00:23:25.020000
2nd row0 days 03:00:21.634000
3rd row0 days 08:38:59.141000
4th row0 days 00:19:27.970000
5th row0 days 00:39:20.469000

Common Values

ValueCountFrequency (%)
0 days 00:00:00.82600062
 
< 0.1%
0 days 00:00:00.35900062
 
< 0.1%
0 days 00:00:00.83700062
 
< 0.1%
0 days 00:00:00.97300061
 
< 0.1%
0 days 00:00:00.76700060
 
< 0.1%
0 days 00:00:00.20400058
 
< 0.1%
0 days 00:00:00.15400057
 
< 0.1%
0 days 00:00:00.51000057
 
< 0.1%
0 days 00:00:30.97700057
 
< 0.1%
0 days 00:00:00.86300057
 
< 0.1%
Other values (1409391)2987588
> 99.9%

Length

2022-10-02T22:01:37.250741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
days2988181
33.3%
02980850
33.3%
13840
 
< 0.1%
21455
 
< 0.1%
3759
 
< 0.1%
4419
 
< 0.1%
5227
 
< 0.1%
6197
 
< 0.1%
7130
 
< 0.1%
00:00:00.82600062
 
< 0.1%
Other values (1408991)2988423
33.3%

Most occurring characters

ValueCountFrequency (%)
022149451
33.7%
5976362
 
9.1%
:5976362
 
9.1%
d2988181
 
4.5%
a2988181
 
4.5%
y2988181
 
4.5%
s2988181
 
4.5%
.2985246
 
4.5%
12773841
 
4.2%
22321243
 
3.5%
Other values (7)11584410
17.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number38828945
59.1%
Lowercase Letter11952724
 
18.2%
Other Punctuation8961608
 
13.6%
Space Separator5976362
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
022149451
57.0%
12773841
 
7.1%
22321243
 
6.0%
32167542
 
5.6%
42034920
 
5.2%
51957277
 
5.0%
61383712
 
3.6%
71361805
 
3.5%
81345390
 
3.5%
91333764
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
d2988181
25.0%
a2988181
25.0%
y2988181
25.0%
s2988181
25.0%
Other Punctuation
ValueCountFrequency (%)
:5976362
66.7%
.2985246
33.3%
Space Separator
ValueCountFrequency (%)
5976362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common53766915
81.8%
Latin11952724
 
18.2%

Most frequent character per script

Common
ValueCountFrequency (%)
022149451
41.2%
5976362
 
11.1%
:5976362
 
11.1%
.2985246
 
5.6%
12773841
 
5.2%
22321243
 
4.3%
32167542
 
4.0%
42034920
 
3.8%
51957277
 
3.6%
61383712
 
2.6%
Other values (3)4040959
 
7.5%
Latin
ValueCountFrequency (%)
d2988181
25.0%
a2988181
25.0%
y2988181
25.0%
s2988181
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII65719639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
022149451
33.7%
5976362
 
9.1%
:5976362
 
9.1%
d2988181
 
4.5%
a2988181
 
4.5%
y2988181
 
4.5%
s2988181
 
4.5%
.2985246
 
4.5%
12773841
 
4.2%
22321243
 
3.5%
Other values (7)11584410
17.6%

session_time
Real number (ℝ)

Distinct52665
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean496.4351383
Minimum-1607899
Maximum1804645
Zeros9270
Zeros (%)0.3%
Negative912713
Negative (%)30.5%
Memory size22.8 MiB
2022-10-02T22:01:37.568741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1607899
5-th percentile-677
Q1-30
median30
Q3266
95-th percentile2520
Maximum1804645
Range3412544
Interquartile range (IQR)296

Descriptive statistics

Standard deviation9565.460867
Coefficient of variation (CV)19.26829938
Kurtosis4064.534365
Mean496.4351383
Median Absolute Deviation (MAD)89
Skewness19.23172964
Sum1483438048
Variance91498041.6
MonotonicityNot monotonic
2022-10-02T22:01:37.893741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30468396
 
15.7%
-30408265
 
13.7%
3111980
 
0.4%
111952
 
0.4%
09270
 
0.3%
436277
 
0.2%
426259
 
0.2%
466254
 
0.2%
456209
 
0.2%
446200
 
0.2%
Other values (52655)2047119
68.5%
ValueCountFrequency (%)
-16078991
< 0.1%
-13016101
< 0.1%
-12899191
< 0.1%
-12260261
< 0.1%
-12193281
< 0.1%
-12084541
< 0.1%
-8833731
< 0.1%
-8796901
< 0.1%
-8745391
< 0.1%
-7833331
< 0.1%
ValueCountFrequency (%)
18046451
< 0.1%
16596061
< 0.1%
13683011
< 0.1%
13209571
< 0.1%
12808231
< 0.1%
12203301
< 0.1%
12182631
< 0.1%
12121491
< 0.1%
11386821
< 0.1%
11213271
< 0.1%

Interactions

2022-10-02T22:00:39.243739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:38.641739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:55.644741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:13.549744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:30.706743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:47.747738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:04.819740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:21.518743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:38.508738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:56.392739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:13.393740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:31.240782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:48.449784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:05.378801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:22.260787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:40.443745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:39.809778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:56.825738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:14.687741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:31.850739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:48.895747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:05.940744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:22.649784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:39.705743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:57.520739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:14.556737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:32.398783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:49.553738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:06.506745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:23.419748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:41.573742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:40.919740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:58.002748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:15.777785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:32.986739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:50.031744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:07.059739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:23.747741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:40.886779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:58.640741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:15.740739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:33.543741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:50.655786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:07.598740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:24.534739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:42.726738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:42.042742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:59.393740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:16.922737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:34.102738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:51.186738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:08.186738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:25.065740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:42.085740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:59.770742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:16.912739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:34.710783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:51.982743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:08.726744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:25.684757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:43.840800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:43.152740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:00.595747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:18.031743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:35.225784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:52.277804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:09.306745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:26.173743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:43.255783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:00.893761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:18.092748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:35.849741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:53.089744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:09.851748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:26.810741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:44.972747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:44.248740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:01.780783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:19.172740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:36.349738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:53.398781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:10.398780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:27.282742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:44.436806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:02.015737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:19.274780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:37.002745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:54.190742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:10.965738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:27.926741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:46.158785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:45.389743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:02.966744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:20.327744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:37.504738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:54.535739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:11.513742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:28.414744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:45.601744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:03.139786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:20.468737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:38.152738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:55.312740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:12.084746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:29.081784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:47.554743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:56:46.531758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:04.167745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:21.476747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:38.670746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:55.678802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:12.622739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:29.537745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:46.796746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:04.252739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:21.655739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:39.305741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:59:56.455743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:13.191741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-02T22:00:51.035738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-02T21:57:07.716740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-02T21:57:42.063738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:57:59.236741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:15.963742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:32.859739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T21:58:50.313739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-02T21:59:25.331786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-02T21:59:59.837739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-02T21:57:08.891737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-02T22:00:21.152739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T22:00:38.080739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-02T22:01:38.226741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-02T22:01:38.772737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-02T22:01:39.187740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-02T22:01:39.597779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-02T22:00:58.196739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-02T22:01:06.583739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0user_idsession_idsession_startsession_sizearticle_idclick_timestampclick_environmentclick_deviceGroupclick_osclick_countryclick_regionclick_referrer_typecategory_idcreated_at_tspublisher_idwords_countdelta_timestampsession_endsession_time
00015068254232717372017-10-01 02:37:0321575412017-10-01 03:00:28.020432012022812017-09-30 19:41:5802800 days 00:00:000 days 00:23:25.0200001405.0
112015068257272797572017-10-01 02:42:0721575412017-10-01 05:42:28.63441171912812017-09-30 19:41:5802800 days 00:00:000 days 03:00:21.63400010822.0
224415068261391857812017-10-01 02:48:5951575412017-10-01 11:27:58.141411711212812017-09-30 19:41:5802800 days 00:00:000 days 08:38:59.14100031139.0
334515068261423247822017-10-01 02:49:0221575412017-10-01 03:08:29.970411711712812017-09-30 19:41:5802800 days 00:00:000 days 00:19:27.9700001168.0
447615068264632268132017-10-01 02:54:2321575412017-10-01 03:33:43.46943212112812017-09-30 19:41:5802800 days 00:00:000 days 00:39:20.4690002360.0
558115068264911018182017-10-01 02:54:5121575412017-10-01 03:47:31.734411712112812017-09-30 19:41:5802800 days 00:00:000 days 00:52:40.7340003161.0
6612115068266531498582017-10-01 02:57:3331575412017-10-01 03:06:43.3022320102822812017-09-30 19:41:5802800 days 00:00:000 days 00:09:10.302000550.0
7714315068267532618802017-10-01 02:59:1331575412017-10-01 03:05:30.024411212122812017-09-30 19:41:5802800 days 00:00:000 days 00:06:17.024000377.0
8815315068267888968902017-10-01 02:59:4831575412017-10-01 03:00:47.697432012422812017-09-30 19:41:5802800 days 00:00:000 days 00:00:59.69700060.0
9915515068267933028922017-10-01 02:59:5321575412017-10-01 03:02:56.135432012522812017-09-30 19:41:5802800 days 00:00:000 days 00:03:03.135000183.0

Last rows

Unnamed: 0user_idsession_idsession_startsession_sizearticle_idclick_timestampclick_environmentclick_deviceGroupclick_osclick_countryclick_regionclick_referrer_typecategory_idcreated_at_tspublisher_idwords_countdelta_timestampsession_endsession_time
298817129881718185415082097285809412017-10-17 03:08:4872072802017-10-17 17:43:24.23241171913312017-10-17 11:43:430291-1 days +23:43:41.6440000 days 14:34:36.232000-978.0
298817229881728185415082097285809412017-10-17 03:08:487687862017-10-17 18:00:12.58841171911362017-10-17 14:40:2801970 days 00:16:48.3560000 days 14:51:24.5880001008.0
298817329881737881415082098363249712017-10-17 03:10:363323982017-10-17 03:41:51.7654321251262017-10-16 23:00:590152-1 days +23:59:300 days 00:31:15.765000-30.0
2988174298817419324715082103083990992017-10-17 03:18:2832894532017-10-17 03:31:40.668411712514212017-10-16 16:29:590211-1 days +23:59:300 days 00:13:12.668000-30.0
2988175298817532285615082103152941022017-10-17 03:18:3532376142017-10-17 03:18:46.97243212173752016-12-28 18:25:430192-1 days +23:56:19.6410000 days 00:00:11.972000-220.0
2988176298817619518615082104224111292017-10-17 03:20:22422212017-10-17 03:21:09.56243211112017-10-16 22:21:090103-1 days +23:55:59.6340000 days 00:00:47.562000-240.0
298817729881777565815082106961851832017-10-17 03:24:5642711172017-10-17 03:29:11.70341171423992017-09-01 14:27:4101560 days 00:01:56.6330000 days 00:04:15.703000117.0
2988178298817821712915082109763362462017-10-17 03:29:362202042017-10-17 03:29:50.810432121592017-04-11 18:13:3002420 days 00:00:000 days 00:00:14.81000015.0
2988179298817921712915082109763362462017-10-17 03:29:362701962017-10-17 03:30:20.81043212151362017-04-04 09:34:5502060 days 00:00:300 days 00:00:44.81000030.0
298818029881805109915082113201933202017-10-17 03:35:202982432017-10-17 03:43:02.52343212552202016-10-18 01:31:25044-1 days +23:59:300 days 00:07:42.523000-30.0